Study MSc Data Science with Placement Year

Course Highlights

Data is the currency of all but the most theoretically-based scientific research and it also underpins our modern world, from the flow of data across international banking networks and the spread of memes across social networks, to the complex models of weather forecasting. The constant generation of data from our digital society feeds into our everyday lives, affecting how we receive healthcare to influencing our shopping habits. In order to handle, make sense of, and exploit large volumes of available data requires highly skilled human insight, analysis and visualisation. The professionals working in this field are called 'data scientists', who blend advanced mathematical and statistical skills with programming, database design, machine learning, modelling, simulation and innovative data visualisation. These professionals are in high demand in both public and private sectors in the UK and worldwide. This programme aims and learning outcomes are built around two guiding principles:

• To provide comprehensive understanding of the fundamental mathematical and statistical concepts underlying data science, and how they are implemented in algorithms and machine learning techniques to solve a variety of data processing and analysis problems.
• To provide training in the practical skills relevant to data science, central of which is the ability to write clean and efficient code in industry-recognised languages (in particular, Python and R), but also includes data handling, manipulation, mining and visualisation techniques.

Course Modules

Learning Structure

The curriculum is structured to ensure that students are exposed to the fundamental mathematical and statistical principles underpinning all data science. These themes will always be relevant in what is a constantly evolving field. Theoretical work will be reinforced with practical application through hands-on laboratories and workshops, to enable you to understand and appreciate how fundamental principles are reflected in a broad range of data processing and analyses. You will become proficient in key practical skills (e.g. use of pandas for working with data structures within Python, and ggplot2 for visualisation in Python and R) using 'real-world' data where possible. In some cases, this data can be sourced from active research projects being conducted by members of teaching staff.

The programme focuses on providing 'end-to-end' training so that you become competent not only in the processing and analysis of data, but also manipulating and preparing data from a raw state as well as interpreting results and effectively communicating findings to others. This will enable you to be prepared for real world challenges and application and will help you to develop independence in your analytical and critical thinking. This will be nurtured in laboratory-based practical sessions so you can put your theories into practice.

Entry Requirements

• A 4-year Honours degree (or equivalent) in a Near-STEM (e.g. Sciences, Tech, Engineering, Mathematics, Physical Sciences, etc) or any relevant discipline with CGPA 3.00 or 60% of marks (or above) from recognised institution.
• A 4-year Honours degree (or equivalent) in a Far-STEM (many different, disparate subjects might have a Data Science relevance, e.g. Business, Geography, etc.) should have relevant working experience.
• Students might possess a non-STEM degree, but must have relevant working experience.
• For far-STEM students who do not possess a good Honours Degree (or equivalent), applications will be assessed on a case-by-case basis. Applicants may be asked to submit a short portfolio providing evidence of:
  1. A basic level of numeracy (e.g. GCSE maths).
  2. Experience and competency with IT/ software (e.g. use of Microsoft Excel).
  3. Experience of a basic interaction with data of any form (e.g. inputting values, making calculations, examining imaging, etc.)

English Language Requirements

Documentary Evidence List

• Academic Transcripts
• Degree Certificate
• English Test (IELTS/ TOEFL/ PTE/ Duolingo - UKVI accepted)
• Statement of Purpose (SOP)
• 2 Recommendation Letters (LORs)
• Updated CV/ Resume
• Research Proposal (for Research/ PhD)
• Work Experience Certificates (if study gap)
• Passport Copy

Study Gaps

For applicants with an academic or professional gap of up to 5 years, admission is generally considered acceptable. If the gap exceeds this period, applications may still be successful but will typically be assessed on a case-by-case basis.

To strengthen your profile, it is important to:

• Provide a clear explanation of how you spent the gap period (e.g., employment, further learning, personal responsibilities).
• Emphasize the skills and experiences you developed during this time that are relevant to the program.
• Demonstrate that your academic qualifications continue to meet the course entry requirements.